Remote Life Sciences Scientist: Wet Lab & Computation

Remotebridge; United States; Remote; USD 83,000 - 165,000; Full time

Job summary

Remotebridge invites applications for a Life Sciences Research Scientist (PhD): Wet Lab & Computational Biology. This remote contractor role focuses on training AI systems by designing expert-level life sciences problems and ensuring robust, literature-grounded solutions.

You will evaluate model outputs for scientific accuracy, develop rubrics, and bridge experimental and computational perspectives while clearly explaining your reasoning to peers.

Qualifications

• PhD in Biology, Molecular Biology, Genetics, Genomics, Biochemistry, Bioinformatics, Computational Biology, Immunology, Neuroscience, Microbiology, or closely related life science field.
• Hands-on research experience in an academic, institutional, or industry research laboratory.
• Professional written and spoken English (C1 or higher).

Responsibilities

• Design and write challenging, expert-level scientific problems in the life sciences with clear, verifiable solutions.
• Review and evaluate AI model outputs for scientific accuracy, rigor, and depth of reasoning.
• Develop detailed rubrics and reference answers grounded in primary literature and established methodology.
• Bridge experimental and computational perspectives: assess whether proposed experiments or analyses are scientifically sound.
• Provide clear written explanations of your reasoning for other experts to follow.
• Meet quality standards through iterative feedback and peer review.

Skills

Hands-on research

English proficiency (C1+)

Education

PhD in Biology/Genomics/related field

Job description

Remotebridge invites applications for a Life Sciences Research Scientist (PhD): Wet Lab & Computational Biology. This remote contractor role focuses on training AI systems by designing expert-level life sciences problems and ensuring robust, literature-grounded solutions.

You will evaluate model outputs for scientific accuracy, develop rubrics, and bridge experimental and computational perspectives while clearly explaining your reasoning to peers.